The purpose of this paper is to present and evaluate an improved Naive Bayes algorithm for clustering. Many researchers search for parameter values using EM *** is well-known that EM approach has a drawback-local opti...
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The purpose of this paper is to present and evaluate an improved Naive Bayes algorithm for clustering. Many researchers search for parameter values using EM *** is well-known that EM approach has a drawback-local optimal solution, so we propose a novel hybrid algorithm of the Discrete particleswarmoptimization (DPSO) and the EM approach to improve the global search performance. We evaluate this hybrid approach on 4 real-world data sets from UCI repository. In a number of experiments and comparisons,the hybrid DPSO+EM algorithm exhibits a more effective and outperforms the EM approach.
Because the network intrusion behaviors are characterized with uncertainty,complexity and diversity,an intrusion detection method based on neural network and particle swarm optimization algorithm(PSOA) is presented in...
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Because the network intrusion behaviors are characterized with uncertainty,complexity and diversity,an intrusion detection method based on neural network and particle swarm optimization algorithm(PSOA) is presented in this *** novel structure model has higher accuracy and faster convergence *** construct the network structure,and give the algorithm *** discussed and analyzed the impact factor of intrusion *** the ability of strong self-learning and faster convergence,this intrusion detection method can detect various intrusion behaviors rapidly and effectively by learning the typical intrusion characteristic *** the character that rough set can keep the discern ability of original dataset after reduction,the reduces of the original dataset are calculated and used to train neural network,which increase the detection *** apply this technique on KDD99 data set and get satisfactory *** experimental result shows that this intrusion detection method is feasible and effective.
Vertical particle swarm optimization algorithm (VPSO) is proposed in this *** new algorithm assumes that the particles tend to fly towards two *** is flying toward the global best *** other is flying toward the vertic...
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Vertical particle swarm optimization algorithm (VPSO) is proposed in this *** new algorithm assumes that the particles tend to fly towards two *** is flying toward the global best *** other is flying toward the vertical *** there is a random value produced in every iteration step to measure the probability of flying into two *** VPSO and particle swarm optimization algorithm (PSO) are used to train neural network (NN) and applied in soft-sensor of acrylonitrile ***, simulation results show that the method proposed by this paper is feasible and effective in soft-sensor of acrylonitrile yield.
Floorplanning is the initial step in the process of designing layout of the chip. It is employed to plan the positions and shapes of modules during the process of VLSI Design cycle to optimize the cost metrics like la...
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Floorplanning is the initial step in the process of designing layout of the chip. It is employed to plan the positions and shapes of modules during the process of VLSI Design cycle to optimize the cost metrics like layout area and wirelength. In this paper, a Hybrid particleswarmoptimization-Firefly (HPSOFF) algorithm is proposed which integrates particleswarmoptimization (PSO), Firefly (FF) and Modified Corner List (MCL) algorithms. Initially, PSO algorithm utilizes MCL algorithm for non-slicing floorplan representations and fitness value evaluation. The solutions obtained from PSO are provided as initial solutions to FF algorithm. Fitness function evaluation and floorplan representations for FF algorithm are again carried out using MCL algorithm. The proposed algorithm is illustrated using Microelectronics Centre of North Carolina (MCNC) and Gigascale Systems Research Centre (GSRC) benchmark circuits. The results obtained are compared with the solutions derived from other stochastic algorithms and the proposed algorithm provides better solutions for both the benchmark circuits.
It is significant to control network congestion by time series forecasting research for network flow. The hybrid method of particle swarm optimization algorithm and RBF neural network is applied to predict network flo...
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It is significant to control network congestion by time series forecasting research for network flow. The hybrid method of particle swarm optimization algorithm and RBF neural network is applied to predict network flow and gain the desirable network flow prediction results. In the hybrid method, particle swarm optimization algorithm is selected and adjusted to the connection weights and the center of radial basis function and the width of radial basis function. The network flow data are collected to search the prediction ability of particle swarm optimization algorithm and RBF neural network. Compared with the results of RBF neural network and BP neural network, particle swarm optimization algorithm and RBF neural network has better forecasting performance.
Image registration based on mutual information is of high accuracy and ***,it has received much attention these ***,the mutual information function is generally not a smooth function but one containing many local maxi...
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Image registration based on mutual information is of high accuracy and ***,it has received much attention these ***,the mutual information function is generally not a smooth function but one containing many local maxima,which has a large influence on *** has fewer parameters to control and can find the best solution quickly and guarantee to be global *** paper proposes a registration method based on wavelet *** this method the mutual information is used as the similarity measure and a hybrid algorithm combined by PSO algorithm and a mutation the search *** method is applied to the 2D registration of *** results show that this registration method could efficiently restrain local maxima of mutual information function and not only it can improve accuracy and speed but also the subvoxel accuracy can be achieved.
A mathematical model of electroslag remelting (ESR) process is established based on its technical features and dynamic characteristics. A new multivariable self-tuning proportional-integral-derivative (PID) controller...
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A mathematical model of electroslag remelting (ESR) process is established based on its technical features and dynamic characteristics. A new multivariable self-tuning proportional-integral-derivative (PID) controller tuned optimally by an improved particleswarmoptimization (IPSO) algorithm is proposed to control the two-input/two-output (TITO) ESR process. An adaptive chaotic migration mutation operator is used to tackle the particles trapped in the clustering field in order to enhance the diversity of the particles in the population, prevent premature convergence and improve the search efficiency of PSO algorithm. The simulation results show the feasibility and effectiveness of the proposed control method. The new method can overcome dynamic working conditions and coupling features of the system in a wide range, and it has strong robustness and adaptability.
As a typical intelligent device, magnetorheological (MR) dampers have been widely applied in vibration control and mitigation. However, the inherent hysteresis characteristics of magnetic materials can cause significa...
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As a typical intelligent device, magnetorheological (MR) dampers have been widely applied in vibration control and mitigation. However, the inherent hysteresis characteristics of magnetic materials can cause significant time delays and fluctuations, affecting the controllability and damping performance of MR dampers. Most existing mathematical models have not considered the adverse effects of magnetic hysteresis characteristics, and this study aims to consider such effects in MR damper models. Based on the magnetic circuit analysis of MR dampers, the Jiles-Atherton (J-A) model is adopted to characterize the magnetic hysteresis properties. Then, a weight adaptive particle swarm optimization algorithm (PSO) is introduced to the J-A model for efficient parameter identifications of this model, in which the differential evolution and the Cauchy variation are combined to improve the diversity of the population and the ability to jump out of the local optimal solution. The results obtained from the improved J-A model are compared with the experimental data under different working conditions, and it shows that the proposed J-A model can accurately predict the damping performance of MR dampers with magnetic hysteresis characteristics.
optimization methodologies are being utilized in various structural designing practices to solve size, shape and topology optimization problems. A heuristic particleswarmoptimization (HPSO) algorithm was anticipated...
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optimization methodologies are being utilized in various structural designing practices to solve size, shape and topology optimization problems. A heuristic particleswarmoptimization (HPSO) algorithm was anticipated in this article in order to address the size optimization problem of truss with stress and displacement constraints. This article contributes in improvisation in the truss structure design rationality while reducing the engineering cost by proposing the HPSO approach. Primarily, the basic principle of the original PSO algorithm is presented, then the compression factor is established to improve the PSO algorithm, and a reasonable parameter setting value is presented. To validate the performance of the proposed optimization approach, various experimental illustrations were performed. The results show that the convergence history of experimental illustration 2 and experimental illustration 3 is optimal. The experimental illustration 2 converges after about 150 iterations, however, the experimental illustration 3 is close to the optimal solution after about 500 iterations. Therefore, the PSO algorithm can successfully optimize the size design of truss structures, and the algorithm is also time efficient. The improved PSO algorithm has good convergence and stability, and can effectively optimize the size design of truss structures.
In fluid mechanics, how to solve power-law fluids in ordinary differential equations is always a concerned and difficult problem. we use generally a shooting method to tackle the boundary-layer problems under a suctio...
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In fluid mechanics, how to solve power-law fluids in ordinary differential equations is always a concerned and difficult problem. we use generally a shooting method to tackle the boundary-layer problems under a suction/injection as well as a reverse flow boundary conditions. A improved particle swarm optimization algorithm(ISPO) is proposed for solving the parameter estimation problems of the multiple solutions in fluid mechanics. This algorithm has improved greatly in precision and the success rate. In this paper, multiple solutions can be found through changing accuracy and search coverage and multi-iterations of computer. Parameter estimation problems of the multiple solutions of ordinary differential equations are calculated, and the result has great accuracy and this method is practical.
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